This invention relates generally to volume rendering in medical imaging, and more particularly, to a method and system for readily generating transfer functions for visualization of rendered volumes.
Mammography is a low-dose x-ray procedure that creates one or more images of a patient's breasts desirable for detection of early stages of cancer. FIG. 1 illustrates one example of a prior art mammography machine 10. Mammography machine 10 generally includes an x-ray tube 12 attached to an arm 14, which arm 14 is pivotally attached to a support 16, and a film plate or digital detector 18 attached to an arm 20, which arm 20 is also pivotally attached to support 16. X-ray tube 12 and arm 14, and digital detector 18 and arm 20, are counterbalanced so that x-ray tube 12 and digital detector 18 may be easily manually pivoted, upwardly and downwardly, and locked in position at different angular orientations.
A typical mammography procedure takes approximately thirty minutes. The procedure generally includes obtaining two images of each of the patient's breasts, one from above and one from the side. For example, separate images are obtained of each of the patient's breasts with x-ray tube 12 and digital detector 18 disposed in a vertically orientated arrangement along axis A (i.e., cranio-caudal) as shown in FIG. 1. In addition, separate images are obtained of each of the patient's breasts with x-ray tube 12 and digital detector 18 oriented at an angle, e.g., along axis B1 (i.e., medio-lateral oblique) for one of the patient's breasts, and along axis B2 for the patient's other breast.
During the procedure, the patient's breast is compressed between a compression paddle 22, e.g., a piece of radiographically transparent plastic, and digital detector cover 18 to flatten the breast, thereby decreasing the thickness and spreading the breast tissue, making the breast easier to be imaged. In obtaining the images, either from above or from the side, x-ray tube 12 is generally aligned perpendicular or normal to film plate or digital detector 18. A physician or radiologist then reviews the images of the breast, i.e., mammograms, to identify any breast cancer.
While the above described procedure is one of the best methods of detecting early forms of breast cancer, it is still possible for the breast cancer to be missed by a physician or radiologist reviewing the mammograms. For example, breast cancer may be missed by being obscured by radiographically dense, fibroglandular breast tissue, which is superimposed on the structures of interest in the mammogram.
Tomosynthesis breast imaging, in which a plurality of images or projection radiographs are acquired as the x-ray source is moved in an arc relative to the stationary breast and a stationary digital detector, has been studied in an effort to improve early detection of breast cancer. By shifting, scaling, and adding the plurality of projection radiographs, it is possible to reconstruct any plane in the breast being imaged that is parallel to the detector, thereby “removing” superimposed tissue from the structures of interest.
Visualizing micro-calcifications and masses, cysts, and other diagnostically relevant structures of the breast in a series of two-dimensional (2D) planes acquired from tomosynthesis breast imaging provides important diagnostic information. However, the volume of data is generally large and contains a considerable range of data content. Thus, radiologists essentially attempt to conceptually reconstruct these planes into a 3D structure by viewing approximately, for example, 60–80 images (usually 10 images for each cm of compressed breast thickness and each typically has a 2304×1800 matrix size) which makes this task difficult and time-consuming.
Volume rendering provides a three-dimensional (3D) visualization of an object volume. Volume rendering has proven to be particularly useful in medical imaging to screen or diagnose patients using 3D tomography data (e.g. computed tomography (CT), magnetic resonance (MR), and, most recently, 3D digital X-ray Mammography (3DDM)). One of the issues that hinders the usage of the volume rendering technique is the definition of initial transfer functions that would show content of the volumetric data that would be useful in diagnosis.
A transfer function is a mechanism for mapping the data values of individual voxels to colors and opacities. Typically, the transfer functions are generated based on prior knowledge of the 3D tomography data. For instance, in CT the range of the intensity values measured in CT Hounsfield numbers are approximately known and reproducible for a variety of tissue types. Using such prior knowledge enables a user to determine and manipulate transfer functions that are necessary to visualize the rendered volumes.
Unlike CT data, the tissue intensity values are not generally reproducible in MR due to the lack of a corresponding concept to Hounsfield numbers. Generally, soft tissues in MR images have high contrast as well as high signal-to-noise ratio (SNR), particularly when data is acquired by high super-conducting magnets. Therefore, transfer functions may generally be set using a simple linear ramp between the minimum intensity value and the maximum intensity value from the 3D data set to sufficiently visualize the rendered volume. However, for data with low contrast and low SNR data (e.g. 3DDM), such a linear ramp may be unacceptable to volume render the data in a suitable manner. Attempting to manually manipulate the transfer functions is typically not practical as it is time consuming and clinically not feasible.
What is needed, therefore, is a method and system for readily determining transfer functions suitable for volume rendering 3D data that has low SNR and low contrast.